• Online, Self-Paced
Course Description

Predictive models that output discrete classes or categories are classification models. Classification is widely used in the real world for use cases such as sentiment analysis of text and identifying objects in images.

In this course, you will review how classification models can be used to categorize or classify input records. You will learn how metrics such as accuracy, precision, and recall can be used to evaluate classification models and the conditions under which you would choose to use precision and recall over accuracy for model evaluation.

Next, you will use the BigQuery command-line tool bq to create a BigQuery dataset and table and load data into that table. You will see how you can run queries and explore your data, all using the command line. You will use Looker Studio for data visualization and DataPrep to clean and prepare your classification data.

Finally, you will train a binary classification model and a multi-class classification model. You will improve the model's performance by balancing the records in the different categories and by using hyperparameter tuning to find the best model for your data.

Learning Objectives

{"discover the key concepts covered in this course"}

Framework Connections

The materials within this course focus on the NICE Framework Task, Knowledge, and Skill statements identified within the indicated NICE Framework component(s):

Specialty Areas

  • Systems Architecture